appendix-h

Appendix H. Computational Drug Target Discovery

 

This chapter covers

  • The target discovery stage of the drug pipeline, sitting upstream of every clinical trial.
  • Evidence frameworks that translate “is this gene related to the disease?” into “is this target worth a billion-dollar program?”
  • Druggability across small molecules, biologics, antibody-drug conjugates, PROTACs, oligonucleotides, and cell and gene therapies.
  • Likelihood of approval by therapeutic area, biomarker-stratification economics, and indication prioritization
  • Quantifying target novelty against the Tclin/Tchem/Tbio/Tdark spectrum and ignorome
  • Knowledge graphs and link prediction for target identification
  • Synthetic lethality, CRISPR-screen analysis, and combination-target machine learning
  • Virtual-cell perturbation models and the benchmarks that test them

H.1 Why Target Discovery Decides Phase II Outcomes

H.1.1 The Cost of Being Wrong at Target Selection

H.1.2 Five criteria a target has to satisfy

H.1.3 Where ML helps

H.2 Target-disease evidence and the omics stack

H.2.1 The four-part evidence argument

H.2.2 Two frameworks: 5R and GOT-IT

H.2.3 Four distinctions inside target-disease linkage

H.2.4 The omics stack as target-disease evidence

H.2.5 Networks, knowledge graphs, and the Open Targets platform

H.3 Druggability and modality choice

H.3.1 What "druggable" means

H.3.2 Structure prediction and the 2020 / 2024 inflections

H.3.3 Beyond small molecules, modality choice as a first-class question

H.3.4 The TDL classification and the ignorome

H.4 Tissue specificity as a safety filter

H.4.1 GTEx and the Human Protein Atlas

H.4.2 The species-conservation problem

H.4.3 Single-cell and why bulk is not enough

H.4.4 A worked counter-example, NTRK fusion-driven cancer

H.4.5 Cross-modal tissue safety

H.5 Likelihood of approval and therapeutic area

H.5.1 The crowded-versus-abandoned map of therapeutic areas

H.5.2 Biomarker stratification and the 2× LOA effect

H.5.3 Indication prioritization

H.6 Target novelty and drug repurposing

H.6.1 Two kinds of novelty (Agarwal versus the TDL pyramid)

H.6.2 Quantifying novelty with NLP

H.6.3 Drug repurposing as the opposite axis

H.6.4 Three computational families for repurposing